Abstract
Beam pumping unit is the most popular oil recovery equipment. One of the most common problems of beam pumping unit is its high energy consumption due to its low system efficiency. The main objective of this study is modeling and optimization a beam pumping unit using Artificial Neural Network (ANN). Among the various networks and architectures, multilayer feed-forward neural network with Back Propagation (BP) training algorithm was found as the best model for the plant. In the next step of study, optimization is performed to identify the sets of optimum operating parameters by Strength Pareto Evolutionary Algorithm-2 (SPEA2) strategy to maximize the oil yield as well as minimize the electric power consumption. Forty-nine sets of optimum conditions are found in our experiments.
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Acknowledgments
The authors would like to acknowledge DaGang oil field for providing industrial data. Thanks to the support by National Natural Science Foundation of China (No. 51075418 and No. 61174015), Chongqing Natural Science Foundation (cstc2013jcyA40044) and Project Foundation of Chongqing Municipal Education Committee (KJ121402).
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Gu, Xh., Liao, Zq., Hu, S., Yi, J., Li, Tf. (2014). Decision Parameter Optimization of Beam Pumping Unit Based on BP Networks Model. In: Cao, BY., Nasseri, H. (eds) Fuzzy Information & Engineering and Operations Research & Management. Advances in Intelligent Systems and Computing, vol 211. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38667-1_2
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DOI: https://doi.org/10.1007/978-3-642-38667-1_2
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